Overview

Dataset statistics

Number of variables20
Number of observations295391
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.1 MiB
Average record size in memory160.0 B

Variable types

Numeric20

Alerts

Time (s) is highly overall correlated with CO (ppm) and 2 other fieldsHigh correlation
CO (ppm) is highly overall correlated with Time (s) and 9 other fieldsHigh correlation
Temperature (C) is highly overall correlated with Time (s) and 2 other fieldsHigh correlation
Heater voltage (V) is highly overall correlated with R4 (MOhm) and 8 other fieldsHigh correlation
R1 (MOhm) is highly overall correlated with R2 (MOhm) and 8 other fieldsHigh correlation
R2 (MOhm) is highly overall correlated with R1 (MOhm) and 6 other fieldsHigh correlation
R3 (MOhm) is highly overall correlated with R1 (MOhm) and 6 other fieldsHigh correlation
R4 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R5 (MOhm) is highly overall correlated with R1 (MOhm) and 12 other fieldsHigh correlation
R6 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R7 (MOhm) is highly overall correlated with R1 (MOhm) and 12 other fieldsHigh correlation
R8 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R9 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R10 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R11 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R12 (MOhm) is highly overall correlated with CO (ppm) and 13 other fieldsHigh correlation
R13 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R14 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
Humidity (%r.h.) is highly overall correlated with Time (s) and 2 other fieldsHigh correlation
Flow rate (mL/min) is highly skewed (γ1 = -108.0920127)Skewed
Time (s) is uniformly distributedUniform
Time (s) has unique valuesUnique
CO (ppm) has 32051 (10.9%) zerosZeros

Reproduction

Analysis started2022-12-20 08:41:56.227857
Analysis finished2022-12-20 08:43:44.646509
Duration1 minute and 48.42 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Time (s)
Real number (ℝ)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct295391
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45419.877
Minimum0
Maximum90909.729
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:44.782633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4547.7225
Q122693.823
median45402.668
Q368123.257
95-th percentile86370.709
Maximum90909.729
Range90909.729
Interquartile range (IQR)45429.434

Descriptive statistics

Standard deviation26237.654
Coefficient of variation (CV)0.57766899
Kurtosis-1.1991592
Mean45419.877
Median Absolute Deviation (MAD)22714.868
Skewness0.0019813493
Sum1.3416623 × 1010
Variance6.8841451 × 108
MonotonicityStrictly increasing
2022-12-20T14:13:45.067151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
60486.328 1
 
< 0.1%
60546.103 1
 
< 0.1%
60545.798 1
 
< 0.1%
60545.493 1
 
< 0.1%
60545.188 1
 
< 0.1%
60544.884 1
 
< 0.1%
60544.579 1
 
< 0.1%
60544.274 1
 
< 0.1%
60543.968 1
 
< 0.1%
Other values (295381) 295381
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.308 1
< 0.1%
0.618 1
< 0.1%
0.926 1
< 0.1%
1.236 1
< 0.1%
1.544 1
< 0.1%
1.854 1
< 0.1%
2.163 1
< 0.1%
2.472 1
< 0.1%
2.78 1
< 0.1%
ValueCountFrequency (%)
90909.729 1
< 0.1%
90909.421 1
< 0.1%
90909.112 1
< 0.1%
90908.802 1
< 0.1%
90908.495 1
< 0.1%
90908.186 1
< 0.1%
90907.876 1
< 0.1%
90907.567 1
< 0.1%
90907.258 1
< 0.1%
90906.95 1
< 0.1%

CO (ppm)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct305
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9090773
Minimum0
Maximum20
Zeros32051
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:45.238204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.44
median8.89
Q315.56
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)11.12

Descriptive statistics

Standard deviation6.4254222
Coefficient of variation (CV)0.64843799
Kurtosis-1.2322066
Mean9.9090773
Median Absolute Deviation (MAD)6.67
Skewness0.0074970252
Sum2927052.3
Variance41.28605
MonotonicityNot monotonic
2022-12-20T14:13:45.402197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32051
10.9%
20 29278
9.9%
15.56 29265
9.9%
13.33 29259
9.9%
11.11 29256
9.9%
8.89 29245
9.9%
6.67 29236
9.9%
17.78 29192
9.9%
4.44 29184
9.9%
2.22 29129
9.9%
Other values (295) 296
 
0.1%
ValueCountFrequency (%)
0 32051
10.9%
0.0111 1
 
< 0.1%
0.0178 1
 
< 0.1%
0.1333 1
 
< 0.1%
0.1776 1
 
< 0.1%
0.2708 1
 
< 0.1%
0.3996 1
 
< 0.1%
0.4218 1
 
< 0.1%
0.4444 1
 
< 0.1%
0.5332 1
 
< 0.1%
ValueCountFrequency (%)
20 29278
9.9%
19.68 1
 
< 0.1%
19.6071 1
 
< 0.1%
19.2319 1
 
< 0.1%
19.1875 1
 
< 0.1%
18.93 1
 
< 0.1%
18.2529 1
 
< 0.1%
18.0272 1
 
< 0.1%
17.9738 1
 
< 0.1%
17.8555 1
 
< 0.1%

Humidity (%r.h.)
Real number (ℝ)

Distinct16650
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.017617
Minimum16.35
Maximum71.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:45.677082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16.35
5-th percentile23
Q136
median46.01
Q354.7
95-th percentile64.2
Maximum71.29
Range54.94
Interquartile range (IQR)18.7

Descriptive statistics

Standard deviation12.300888
Coefficient of variation (CV)0.2732461
Kurtosis-0.7437183
Mean45.017617
Median Absolute Deviation (MAD)9.4
Skewness-0.16509868
Sum13297799
Variance151.31185
MonotonicityNot monotonic
2022-12-20T14:13:45.825638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.9 4163
 
1.4%
36 3164
 
1.1%
37.06 2647
 
0.9%
29.62 2383
 
0.8%
50.67 2241
 
0.8%
31.72 1852
 
0.6%
30.64 1823
 
0.6%
45.49 1680
 
0.6%
38.12 1621
 
0.5%
33.87 1557
 
0.5%
Other values (16640) 272260
92.2%
ValueCountFrequency (%)
16.35 55
< 0.1%
16.3504 1
 
< 0.1%
16.3506 1
 
< 0.1%
16.3519 1
 
< 0.1%
16.3523 1
 
< 0.1%
16.3526 1
 
< 0.1%
16.3535 1
 
< 0.1%
16.3537 1
 
< 0.1%
16.355 1
 
< 0.1%
16.3554 1
 
< 0.1%
ValueCountFrequency (%)
71.29 592
0.2%
71.2613 1
 
< 0.1%
71.2519 1
 
< 0.1%
71.1946 1
 
< 0.1%
71.1805 1
 
< 0.1%
71.1161 1
 
< 0.1%
71.1067 1
 
< 0.1%
71.0503 1
 
< 0.1%
71.0353 1
 
< 0.1%
70.9704 1
 
< 0.1%

Temperature (C)
Real number (ℝ)

Distinct6573
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.550344
Minimum25.02
Maximum26.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:45.988825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum25.02
5-th percentile25.02
Q125.14
median25.58
Q325.9
95-th percentile26.14
Maximum26.26
Range1.24
Interquartile range (IQR)0.76

Descriptive statistics

Standard deviation0.39901247
Coefficient of variation (CV)0.015616716
Kurtosis-1.4433366
Mean25.550344
Median Absolute Deviation (MAD)0.4
Skewness0.13624943
Sum7547341.7
Variance0.15921095
MonotonicityNot monotonic
2022-12-20T14:13:46.156277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.06 21772
 
7.4%
25.02 20070
 
6.8%
25.1 18898
 
6.4%
25.18 17362
 
5.9%
26.14 15792
 
5.3%
25.14 15303
 
5.2%
25.62 13901
 
4.7%
25.5 12160
 
4.1%
25.82 11562
 
3.9%
26.1 11039
 
3.7%
Other values (6563) 137532
46.6%
ValueCountFrequency (%)
25.02 20070
6.8%
25.0201 1
 
< 0.1%
25.0202 3
 
< 0.1%
25.0203 1
 
< 0.1%
25.0204 1
 
< 0.1%
25.0205 1
 
< 0.1%
25.0206 5
 
< 0.1%
25.0207 3
 
< 0.1%
25.0208 2
 
< 0.1%
25.0209 2
 
< 0.1%
ValueCountFrequency (%)
26.26 26
< 0.1%
26.2581 1
 
< 0.1%
26.2566 1
 
< 0.1%
26.2505 1
 
< 0.1%
26.249 1
 
< 0.1%
26.2457 1
 
< 0.1%
26.2442 1
 
< 0.1%
26.2382 1
 
< 0.1%
26.2366 1
 
< 0.1%
26.2333 1
 
< 0.1%

Flow rate (mL/min)
Real number (ℝ)

Distinct11204
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.94697
Minimum0
Maximum275.3594
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:46.316099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile239.7554
Q1239.8955
median239.9717
Q3240.0445
95-th percentile240.1799
Maximum275.3594
Range275.3594
Interquartile range (IQR)0.149

Descriptive statistics

Standard deviation1.7253801
Coefficient of variation (CV)0.0071906727
Kurtosis13719.531
Mean239.94697
Median Absolute Deviation (MAD)0.0745
Skewness-108.09201
Sum70878176
Variance2.9769366
MonotonicityNot monotonic
2022-12-20T14:13:46.464111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239.9494 144
 
< 0.1%
239.9672 142
 
< 0.1%
239.9801 139
 
< 0.1%
239.9765 138
 
< 0.1%
239.9208 136
 
< 0.1%
239.9928 135
 
< 0.1%
239.9768 135
 
< 0.1%
239.9576 134
 
< 0.1%
239.966 133
 
< 0.1%
239.983 133
 
< 0.1%
Other values (11194) 294022
99.5%
ValueCountFrequency (%)
0 5
< 0.1%
0.0039 1
 
< 0.1%
0.0368 1
 
< 0.1%
0.0695 1
 
< 0.1%
0.1023 1
 
< 0.1%
37.5555 1
 
< 0.1%
41.5409 1
 
< 0.1%
65.5496 1
 
< 0.1%
83.9925 1
 
< 0.1%
110.8883 1
 
< 0.1%
ValueCountFrequency (%)
275.3594 1
< 0.1%
269.0101 1
< 0.1%
263.4168 1
< 0.1%
261.1642 1
< 0.1%
261.1136 1
< 0.1%
258.9654 1
< 0.1%
257.4505 1
< 0.1%
256.9294 1
< 0.1%
255.575 1
< 0.1%
255.369 1
< 0.1%

Heater voltage (V)
Real number (ℝ)

Distinct1727
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35488267
Minimum0.198
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:46.626996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.198
5-th percentile0.1991
Q10.2
median0.2
Q30.207
95-th percentile0.8984
Maximum0.9
Range0.702
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation0.28840982
Coefficient of variation (CV)0.81269065
Kurtosis-0.20552806
Mean0.35488267
Median Absolute Deviation (MAD)0.0003
Skewness1.3372745
Sum104829.15
Variance0.083180227
MonotonicityNot monotonic
2022-12-20T14:13:46.782086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 134047
45.4%
0.898 25858
 
8.8%
0.199 10540
 
3.6%
0.899 6154
 
2.1%
0.1996 5798
 
2.0%
0.1991 5779
 
2.0%
0.1999 5721
 
1.9%
0.1997 5693
 
1.9%
0.1993 5692
 
1.9%
0.1995 5690
 
1.9%
Other values (1717) 84419
28.6%
ValueCountFrequency (%)
0.198 1
 
< 0.1%
0.1981 2
 
< 0.1%
0.1982 2
 
< 0.1%
0.1983 1
 
< 0.1%
0.1984 7
 
< 0.1%
0.1985 2
 
< 0.1%
0.1986 2
 
< 0.1%
0.1987 4
 
< 0.1%
0.1988 1
 
< 0.1%
0.199 10540
3.6%
ValueCountFrequency (%)
0.9 6
< 0.1%
0.8999 8
< 0.1%
0.8998 4
 
< 0.1%
0.8997 10
< 0.1%
0.8996 8
< 0.1%
0.8995 11
< 0.1%
0.8994 14
< 0.1%
0.8993 14
< 0.1%
0.8992 8
< 0.1%
0.8991 12
< 0.1%

R1 (MOhm)
Real number (ℝ)

Distinct8497
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.370183
Minimum0.0327
Maximum119.5851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:46.948997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0327
5-th percentile0.079
Q10.4397
median2.3278
Q331.4259
95-th percentile71.9176
Maximum119.5851
Range119.5524
Interquartile range (IQR)30.9862

Descriptive statistics

Standard deviation24.680644
Coefficient of variation (CV)1.4208626
Kurtosis0.70373264
Mean17.370183
Median Absolute Deviation (MAD)2.2461
Skewness1.3671022
Sum5130995.6
Variance609.13417
MonotonicityNot monotonic
2022-12-20T14:13:47.113932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.6194 791
 
0.3%
74.3444 760
 
0.3%
75.0345 739
 
0.3%
73.1111 736
 
0.2%
70.7619 720
 
0.2%
76.3332 718
 
0.2%
73.7788 713
 
0.2%
71.9176 710
 
0.2%
71.3877 705
 
0.2%
72.5638 695
 
0.2%
Other values (8487) 288104
97.5%
ValueCountFrequency (%)
0.0327 1
< 0.1%
0.0332 1
< 0.1%
0.0333 1
< 0.1%
0.0336 2
< 0.1%
0.0338 1
< 0.1%
0.0339 2
< 0.1%
0.034 1
< 0.1%
0.0341 2
< 0.1%
0.0342 1
< 0.1%
0.0344 1
< 0.1%
ValueCountFrequency (%)
119.5851 2
 
< 0.1%
117.8584 1
 
< 0.1%
116.4568 4
 
< 0.1%
114.818 12
 
< 0.1%
113.4868 7
 
< 0.1%
111.9292 13
 
< 0.1%
110.6632 21
< 0.1%
109.181 47
< 0.1%
107.9756 49
< 0.1%
106.5634 37
< 0.1%

R2 (MOhm)
Real number (ℝ)

Distinct8234
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.227858
Minimum0.0585
Maximum152.2348
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:47.282798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0585
5-th percentile0.1407
Q10.5079
median1.6824
Q335.4875
95-th percentile80.5097
Maximum152.2348
Range152.1763
Interquartile range (IQR)34.9796

Descriptive statistics

Standard deviation28.149495
Coefficient of variation (CV)1.4639954
Kurtosis0.19036531
Mean19.227858
Median Absolute Deviation (MAD)1.5402
Skewness1.2826163
Sum5679736.2
Variance792.39405
MonotonicityNot monotonic
2022-12-20T14:13:47.441361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81.1822 1186
 
0.4%
82.7015 1170
 
0.4%
82.004 1154
 
0.4%
83.5541 1101
 
0.4%
80.5097 1101
 
0.4%
79.0683 1096
 
0.4%
79.7171 1056
 
0.4%
78.3034 1031
 
0.3%
84.278 1017
 
0.3%
76.9383 1003
 
0.3%
Other values (8224) 284476
96.3%
ValueCountFrequency (%)
0.0585 1
< 0.1%
0.0587 1
< 0.1%
0.059 1
< 0.1%
0.0592 1
< 0.1%
0.0594 1
< 0.1%
0.0597 1
< 0.1%
0.0598 1
< 0.1%
0.0601 2
< 0.1%
0.0608 1
< 0.1%
0.0611 1
< 0.1%
ValueCountFrequency (%)
152.2348 1
 
< 0.1%
142.5199 1
 
< 0.1%
133.9633 2
< 0.1%
131.804 1
 
< 0.1%
128.0195 1
 
< 0.1%
124.4448 1
 
< 0.1%
121.0628 3
< 0.1%
117.8584 4
< 0.1%
116.4568 1
 
< 0.1%
114.818 3
< 0.1%

R3 (MOhm)
Real number (ℝ)

Distinct8219
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.725092
Minimum0.0545
Maximum171.4839
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:47.617488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0545
5-th percentile0.1121
Q10.6236
median5.2616
Q348.4112
95-th percentile83.0507
Maximum171.4839
Range171.4294
Interquartile range (IQR)47.7876

Descriptive statistics

Standard deviation29.515198
Coefficient of variation (CV)1.2440499
Kurtosis-0.50224069
Mean23.725092
Median Absolute Deviation (MAD)5.1478
Skewness0.96676508
Sum7008178.6
Variance871.14693
MonotonicityNot monotonic
2022-12-20T14:13:47.871037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84.6501 1221
 
0.4%
85.3974 1221
 
0.4%
83.7703 1203
 
0.4%
86.3116 1163
 
0.4%
82.2033 1144
 
0.4%
83.0507 1132
 
0.4%
81.51 1107
 
0.4%
87.0883 1046
 
0.4%
80.6932 1043
 
0.4%
88.0388 1032
 
0.3%
Other values (8209) 284079
96.2%
ValueCountFrequency (%)
0.0545 1
< 0.1%
0.0547 1
< 0.1%
0.0551 1
< 0.1%
0.0555 1
< 0.1%
0.0556 1
< 0.1%
0.0558 1
< 0.1%
0.0561 1
< 0.1%
0.0562 1
< 0.1%
0.0563 2
< 0.1%
0.0568 1
< 0.1%
ValueCountFrequency (%)
171.4839 1
 
< 0.1%
125.6085 2
 
< 0.1%
123.6951 1
 
< 0.1%
115.7553 1
 
< 0.1%
114.1263 1
 
< 0.1%
112.8031 10
 
< 0.1%
111.2549 17
 
< 0.1%
109.9965 27
< 0.1%
108.5233 59
< 0.1%
107.3251 54
< 0.1%

R4 (MOhm)
Real number (ℝ)

Distinct7530
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.757554
Minimum0.0395
Maximum88.499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:48.049282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0395
5-th percentile0.1019
Q12.2035
median23.353
Q336.4987
95-th percentile54.5125
Maximum88.499
Range88.4595
Interquartile range (IQR)34.2952

Descriptive statistics

Standard deviation18.641809
Coefficient of variation (CV)0.81914815
Kurtosis-0.86398891
Mean22.757554
Median Absolute Deviation (MAD)16.4022
Skewness0.3279607
Sum6722376.7
Variance347.51703
MonotonicityNot monotonic
2022-12-20T14:13:48.204907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.2375 1121
 
0.4%
36.1399 1067
 
0.4%
36.8645 1065
 
0.4%
36.3021 1057
 
0.4%
36.6641 1052
 
0.4%
35.7879 1040
 
0.4%
35.9471 1037
 
0.4%
35.1038 1034
 
0.4%
37.0331 1034
 
0.4%
36.4987 1022
 
0.3%
Other values (7520) 284862
96.4%
ValueCountFrequency (%)
0.0395 1
< 0.1%
0.0401 1
< 0.1%
0.0403 1
< 0.1%
0.0406 1
< 0.1%
0.0407 1
< 0.1%
0.0408 1
< 0.1%
0.041 1
< 0.1%
0.0411 1
< 0.1%
0.0413 1
< 0.1%
0.0415 1
< 0.1%
ValueCountFrequency (%)
88.499 1
 
< 0.1%
87.5664 3
 
< 0.1%
84.5361 6
 
< 0.1%
83.6839 6
 
< 0.1%
82.6836 12
 
< 0.1%
81.8678 19
 
< 0.1%
80.9098 30
< 0.1%
80.1281 31
< 0.1%
79.2097 40
< 0.1%
78.4601 50
< 0.1%

R5 (MOhm)
Real number (ℝ)

Distinct7792
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.667017
Minimum0.0482
Maximum183.0801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:48.365746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0482
5-th percentile0.1152
Q12.00825
median36.151
Q354.3512
95-th percentile81.8393
Maximum183.0801
Range183.0319
Interquartile range (IQR)52.34295

Descriptive statistics

Standard deviation28.279155
Coefficient of variation (CV)0.83996616
Kurtosis-1.0165976
Mean33.667017
Median Absolute Deviation (MAD)26.0062
Skewness0.29551739
Sum9944933.8
Variance799.71061
MonotonicityNot monotonic
2022-12-20T14:13:48.527349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.1571 1441
 
0.5%
50.8849 1416
 
0.5%
50.296 1399
 
0.5%
49.4117 1396
 
0.5%
53.0277 1395
 
0.5%
51.7661 1376
 
0.5%
49.9804 1376
 
0.5%
49.7203 1370
 
0.5%
52.1043 1369
 
0.5%
51.4875 1367
 
0.5%
Other values (7782) 281486
95.3%
ValueCountFrequency (%)
0.0482 1
 
< 0.1%
0.0499 1
 
< 0.1%
0.0505 3
< 0.1%
0.0506 2
< 0.1%
0.0507 1
 
< 0.1%
0.0508 1
 
< 0.1%
0.051 3
< 0.1%
0.0511 1
 
< 0.1%
0.0512 1
 
< 0.1%
0.0513 1
 
< 0.1%
ValueCountFrequency (%)
183.0801 1
 
< 0.1%
131.5393 1
 
< 0.1%
129.7955 2
 
< 0.1%
127.7625 1
 
< 0.1%
126.116 5
 
< 0.1%
124.1949 7
 
< 0.1%
122.6378 12
< 0.1%
120.8197 18
< 0.1%
119.345 26
< 0.1%
117.6218 28
< 0.1%

R6 (MOhm)
Real number (ℝ)

Distinct7809
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.291505
Minimum0.0468
Maximum183.1201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:48.704135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0468
5-th percentile0.124
Q11.668
median26.1194
Q351.7707
95-th percentile80.6531
Maximum183.1201
Range183.0733
Interquartile range (IQR)50.1027

Descriptive statistics

Standard deviation27.948096
Coefficient of variation (CV)0.92263806
Kurtosis-0.94291559
Mean30.291505
Median Absolute Deviation (MAD)24.8459
Skewness0.49305968
Sum8947838
Variance781.09605
MonotonicityNot monotonic
2022-12-20T14:13:48.865269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.8369 1243
 
0.4%
49.6055 1238
 
0.4%
48.6935 1222
 
0.4%
50.2138 1211
 
0.4%
80.6531 1197
 
0.4%
51.1251 1196
 
0.4%
49.8802 1192
 
0.4%
52.1297 1184
 
0.4%
51.4753 1173
 
0.4%
48.4314 1168
 
0.4%
Other values (7799) 283367
95.9%
ValueCountFrequency (%)
0.0468 1
 
< 0.1%
0.0473 1
 
< 0.1%
0.048 1
 
< 0.1%
0.0486 1
 
< 0.1%
0.0491 1
 
< 0.1%
0.0493 1
 
< 0.1%
0.0496 1
 
< 0.1%
0.05 3
< 0.1%
0.0501 1
 
< 0.1%
0.0502 1
 
< 0.1%
ValueCountFrequency (%)
183.1201 1
 
< 0.1%
147.9504 1
 
< 0.1%
129.6516 1
 
< 0.1%
123.7317 1
 
< 0.1%
118.6302 1
 
< 0.1%
115.3585 12
 
< 0.1%
113.6483 14
 
< 0.1%
112.2611 16
 
< 0.1%
110.6402 45
< 0.1%
109.3244 69
< 0.1%

R7 (MOhm)
Real number (ℝ)

Distinct7717
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.764127
Minimum0.0527
Maximum228.7794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:49.036882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0527
5-th percentile0.1226
Q12.1207
median35.3355
Q355.0846
95-th percentile82.883
Maximum228.7794
Range228.7267
Interquartile range (IQR)52.9639

Descriptive statistics

Standard deviation28.465058
Coefficient of variation (CV)0.84305622
Kurtosis-1.0783578
Mean33.764127
Median Absolute Deviation (MAD)26.4465
Skewness0.29132787
Sum9973619.3
Variance810.2595
MonotonicityNot monotonic
2022-12-20T14:13:49.193952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.4536 1461
 
0.5%
52.4174 1429
 
0.5%
52.7077 1413
 
0.5%
50.5778 1398
 
0.5%
51.7898 1386
 
0.5%
50.8483 1385
 
0.5%
49.6787 1381
 
0.5%
53.3574 1378
 
0.5%
53.0601 1367
 
0.5%
53.7185 1359
 
0.5%
Other values (7707) 281434
95.3%
ValueCountFrequency (%)
0.0527 1
< 0.1%
0.0531 1
< 0.1%
0.0532 1
< 0.1%
0.0533 1
< 0.1%
0.0539 1
< 0.1%
0.054 1
< 0.1%
0.0544 1
< 0.1%
0.0545 1
< 0.1%
0.055 2
< 0.1%
0.0551 1
< 0.1%
ValueCountFrequency (%)
228.7794 1
 
< 0.1%
143.4564 1
 
< 0.1%
115.5214 5
 
< 0.1%
113.8957 12
 
< 0.1%
112.5752 21
 
< 0.1%
111.0301 32
 
< 0.1%
109.7743 57
< 0.1%
108.304 73
< 0.1%
107.1083 122
< 0.1%
105.7075 140
< 0.1%

R8 (MOhm)
Real number (ℝ)

Distinct6245
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.377198
Minimum0.0337
Maximum114.3243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:49.353161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0337
5-th percentile0.0998
Q112.8942
median29.085
Q344.7437
95-th percentile66.0689
Maximum114.3243
Range114.2906
Interquartile range (IQR)31.8495

Descriptive statistics

Standard deviation21.545517
Coefficient of variation (CV)0.73340953
Kurtosis-0.80876465
Mean29.377198
Median Absolute Deviation (MAD)15.6587
Skewness0.1771586
Sum8677759.9
Variance464.20931
MonotonicityNot monotonic
2022-12-20T14:13:49.513036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.7445 1103
 
0.4%
55.6363 1097
 
0.4%
54.073 1090
 
0.4%
55.988 1088
 
0.4%
54.8097 1075
 
0.4%
56.7773 1075
 
0.4%
51.311 1072
 
0.4%
52.3446 1066
 
0.4%
50.3742 1056
 
0.4%
56.4158 1055
 
0.4%
Other values (6235) 284614
96.4%
ValueCountFrequency (%)
0.0337 1
 
< 0.1%
0.0339 1
 
< 0.1%
0.0341 1
 
< 0.1%
0.0342 2
< 0.1%
0.0344 3
< 0.1%
0.0346 2
< 0.1%
0.0347 1
 
< 0.1%
0.035 1
 
< 0.1%
0.0351 3
< 0.1%
0.0352 1
 
< 0.1%
ValueCountFrequency (%)
114.3243 1
 
< 0.1%
100.3018 1
 
< 0.1%
99.1944 2
 
< 0.1%
97.8971 1
 
< 0.1%
96.8414 4
 
< 0.1%
95.604 6
 
< 0.1%
94.5965 4
 
< 0.1%
93.4149 8
 
< 0.1%
92.4523 10
< 0.1%
91.3229 20
< 0.1%

R9 (MOhm)
Real number (ℝ)

Distinct6162
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.205256
Minimum0.0292
Maximum115.1069
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:49.677223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0292
5-th percentile0.0967
Q18.6458
median23.36
Q339.0446
95-th percentile62.2706
Maximum115.1069
Range115.0777
Interquartile range (IQR)30.3988

Descriptive statistics

Standard deviation19.933879
Coefficient of variation (CV)0.79086197
Kurtosis-0.70809062
Mean25.205256
Median Absolute Deviation (MAD)15.4911
Skewness0.41184375
Sum7445405.9
Variance397.35952
MonotonicityNot monotonic
2022-12-20T14:13:49.840179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0972 2875
 
1.0%
0.0971 2829
 
1.0%
0.0973 2765
 
0.9%
0.0975 2707
 
0.9%
0.097 2695
 
0.9%
0.0976 2579
 
0.9%
0.0977 2549
 
0.9%
0.0968 2499
 
0.8%
0.0978 2430
 
0.8%
0.098 2252
 
0.8%
Other values (6152) 269211
91.1%
ValueCountFrequency (%)
0.0292 2
 
< 0.1%
0.0297 3
< 0.1%
0.0298 1
 
< 0.1%
0.0299 5
< 0.1%
0.03 2
 
< 0.1%
0.0301 4
< 0.1%
0.0302 4
< 0.1%
0.0303 3
< 0.1%
0.0304 6
< 0.1%
0.0306 1
 
< 0.1%
ValueCountFrequency (%)
115.1069 1
 
< 0.1%
84.9877 1
 
< 0.1%
83.3057 3
 
< 0.1%
82.5627 3
 
< 0.1%
81.6883 3
 
< 0.1%
80.9734 9
 
< 0.1%
80.1318 12
 
< 0.1%
79.4435 14
 
< 0.1%
78.6328 18
< 0.1%
77.9697 40
< 0.1%

R10 (MOhm)
Real number (ℝ)

Distinct6396
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.06686
Minimum0.0383
Maximum148.5568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:50.107605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0383
5-th percentile0.1184
Q17.9612
median24.7109
Q342.6608
95-th percentile66.0832
Maximum148.5568
Range148.5185
Interquartile range (IQR)34.6996

Descriptive statistics

Standard deviation21.660884
Coefficient of variation (CV)0.80027324
Kurtosis-0.78144451
Mean27.06686
Median Absolute Deviation (MAD)17.5305
Skewness0.40579156
Sum7995306.9
Variance469.19389
MonotonicityNot monotonic
2022-12-20T14:13:50.264953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1199 1818
 
0.6%
0.1201 1725
 
0.6%
0.1204 1721
 
0.6%
0.1198 1716
 
0.6%
0.1202 1673
 
0.6%
0.1205 1671
 
0.6%
0.1206 1654
 
0.6%
0.1197 1624
 
0.5%
0.1195 1547
 
0.5%
0.1208 1521
 
0.5%
Other values (6386) 278721
94.4%
ValueCountFrequency (%)
0.0383 1
 
< 0.1%
0.0385 1
 
< 0.1%
0.0386 2
< 0.1%
0.0387 2
< 0.1%
0.039 1
 
< 0.1%
0.0391 2
< 0.1%
0.0392 1
 
< 0.1%
0.0393 2
< 0.1%
0.0394 3
< 0.1%
0.0395 3
< 0.1%
ValueCountFrequency (%)
148.5568 1
 
< 0.1%
118.8647 1
 
< 0.1%
112.8031 1
 
< 0.1%
103.4404 1
 
< 0.1%
95.6897 3
 
< 0.1%
94.5698 2
 
< 0.1%
93.6563 4
 
< 0.1%
92.5828 11
< 0.1%
91.7066 6
< 0.1%
90.6767 4
 
< 0.1%

R11 (MOhm)
Real number (ℝ)

Distinct6217
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.139853
Minimum0.0308
Maximum119.2249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:50.436180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0308
5-th percentile0.1082
Q111.102
median28.5782
Q345.0218
95-th percentile66.8349
Maximum119.2249
Range119.1941
Interquartile range (IQR)33.9198

Descriptive statistics

Standard deviation21.762868
Coefficient of variation (CV)0.74684208
Kurtosis-0.84737328
Mean29.139853
Median Absolute Deviation (MAD)16.6579
Skewness0.21542219
Sum8607650.3
Variance473.62244
MonotonicityNot monotonic
2022-12-20T14:13:50.603236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1085 2481
 
0.8%
0.1092 2434
 
0.8%
0.109 2399
 
0.8%
0.1086 2355
 
0.8%
0.1084 2334
 
0.8%
0.1089 2299
 
0.8%
0.1093 2250
 
0.8%
0.1088 2232
 
0.8%
0.1082 2123
 
0.7%
0.1094 1879
 
0.6%
Other values (6207) 272605
92.3%
ValueCountFrequency (%)
0.0308 1
 
< 0.1%
0.031 1
 
< 0.1%
0.0313 1
 
< 0.1%
0.0315 1
 
< 0.1%
0.0316 3
< 0.1%
0.0318 1
 
< 0.1%
0.0319 2
< 0.1%
0.032 3
< 0.1%
0.0321 1
 
< 0.1%
0.0323 1
 
< 0.1%
ValueCountFrequency (%)
119.2249 2
 
< 0.1%
116.1061 1
 
< 0.1%
103.7538 1
 
< 0.1%
93.9401 2
 
< 0.1%
92.8633 6
 
< 0.1%
91.9845 10
 
< 0.1%
90.9514 10
 
< 0.1%
90.1079 28
< 0.1%
89.1159 27
< 0.1%
88.3056 35
< 0.1%

R12 (MOhm)
Real number (ℝ)

Distinct6243
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.377447
Minimum0.0331
Maximum95.0475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:50.773354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0331
5-th percentile0.1078
Q110.2201
median27.6959
Q342.4548
95-th percentile59.235
Maximum95.0475
Range95.0144
Interquartile range (IQR)32.2347

Descriptive statistics

Standard deviation19.984757
Coefficient of variation (CV)0.72997154
Kurtosis-0.86666984
Mean27.377447
Median Absolute Deviation (MAD)15.3746
Skewness0.12346446
Sum8087051.4
Variance399.39051
MonotonicityNot monotonic
2022-12-20T14:13:50.929889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1091 1241
 
0.4%
0.1088 1233
 
0.4%
0.109 1207
 
0.4%
0.1094 1184
 
0.4%
0.1093 1172
 
0.4%
0.1087 1157
 
0.4%
50.3466 1109
 
0.4%
50.0306 1109
 
0.4%
52.1567 1107
 
0.4%
0.1095 1095
 
0.4%
Other values (6233) 283777
96.1%
ValueCountFrequency (%)
0.0331 1
 
< 0.1%
0.0336 1
 
< 0.1%
0.034 2
 
< 0.1%
0.0341 1
 
< 0.1%
0.0343 1
 
< 0.1%
0.0344 2
 
< 0.1%
0.0348 3
< 0.1%
0.0349 1
 
< 0.1%
0.035 3
< 0.1%
0.0351 5
< 0.1%
ValueCountFrequency (%)
95.0475 1
 
< 0.1%
90.2894 4
 
< 0.1%
89.2954 8
 
< 0.1%
88.4834 7
 
< 0.1%
87.5281 13
 
< 0.1%
86.7475 25
< 0.1%
85.8287 24
 
< 0.1%
85.0777 42
< 0.1%
84.1934 48
< 0.1%
83.4702 60
< 0.1%

R13 (MOhm)
Real number (ℝ)

Distinct6373
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.317591
Minimum0.0336
Maximum98.2095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:51.094480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0336
5-th percentile0.1015
Q18.2815
median23.2405
Q337.8786
95-th percentile57.0349
Maximum98.2095
Range98.1759
Interquartile range (IQR)29.5971

Descriptive statistics

Standard deviation18.680398
Coefficient of variation (CV)0.76818456
Kurtosis-0.77891542
Mean24.317591
Median Absolute Deviation (MAD)14.6933
Skewness0.30791627
Sum7183197.4
Variance348.95726
MonotonicityNot monotonic
2022-12-20T14:13:51.259339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.103 1180
 
0.4%
0.1031 1177
 
0.4%
0.1035 1157
 
0.4%
0.1032 1156
 
0.4%
0.1034 1126
 
0.4%
0.1029 1111
 
0.4%
0.1037 1101
 
0.4%
0.1038 1063
 
0.4%
0.1044 1057
 
0.4%
0.1042 1045
 
0.4%
Other values (6363) 284218
96.2%
ValueCountFrequency (%)
0.0336 1
 
< 0.1%
0.0339 1
 
< 0.1%
0.0342 1
 
< 0.1%
0.0349 3
< 0.1%
0.035 1
 
< 0.1%
0.0351 1
 
< 0.1%
0.0352 2
< 0.1%
0.0353 2
< 0.1%
0.0355 3
< 0.1%
0.0356 2
< 0.1%
ValueCountFrequency (%)
98.2095 1
 
< 0.1%
84.1371 1
 
< 0.1%
81.7051 2
 
< 0.1%
81.016 1
 
< 0.1%
79.5397 3
 
< 0.1%
78.7567 6
 
< 0.1%
78.1157 5
 
< 0.1%
77.3599 15
< 0.1%
76.7411 18
< 0.1%
76.0113 20
< 0.1%

R14 (MOhm)
Real number (ℝ)

Distinct6253
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.079873
Minimum0.0316
Maximum122.1958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T14:13:51.422231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0316
5-th percentile0.1072
Q110.0728
median28.3385
Q347.2945
95-th percentile71.2524
Maximum122.1958
Range122.1642
Interquartile range (IQR)37.2217

Descriptive statistics

Standard deviation23.214249
Coefficient of variation (CV)0.77175356
Kurtosis-0.94067535
Mean30.079873
Median Absolute Deviation (MAD)18.8139
Skewness0.28018443
Sum8885323.9
Variance538.90137
MonotonicityNot monotonic
2022-12-20T14:13:51.589604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1075 2384
 
0.8%
0.1077 2368
 
0.8%
0.1079 2353
 
0.8%
0.1078 2322
 
0.8%
0.108 2285
 
0.8%
0.1083 2279
 
0.8%
0.1082 2247
 
0.8%
0.1086 2184
 
0.7%
0.1074 2098
 
0.7%
0.1084 2083
 
0.7%
Other values (6243) 272788
92.3%
ValueCountFrequency (%)
0.0316 1
 
< 0.1%
0.0319 2
 
< 0.1%
0.0322 2
 
< 0.1%
0.0325 3
< 0.1%
0.0326 2
 
< 0.1%
0.0327 2
 
< 0.1%
0.0328 1
 
< 0.1%
0.033 3
< 0.1%
0.0332 4
< 0.1%
0.0333 7
< 0.1%
ValueCountFrequency (%)
122.1958 1
 
< 0.1%
107.2078 1
 
< 0.1%
94.5298 1
 
< 0.1%
91.4606 2
 
< 0.1%
90.595 1
 
< 0.1%
89.5776 5
 
< 0.1%
88.7468 2
 
< 0.1%
87.7697 7
 
< 0.1%
86.9716 14
< 0.1%
86.0327 21
< 0.1%

Interactions

2022-12-20T14:13:38.699056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:29.543530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:32.853973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:36.501657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:40.153152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:43.677542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:47.250134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:50.940941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:54.611128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:58.368331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:02.011695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:05.549761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:09.251050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:12.988586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:16.523521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:20.117993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:23.777844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:27.546489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:31.151204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:34.776203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:38.877682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:29.732596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:33.019773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:36.652825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:40.317385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:43.843810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:47.420898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:51.115705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:54.789178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:58.538563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:02.178372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:05.723646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:09.426826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:13.151941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:16.691097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:20.284855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:23.955686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:27.709893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:31.317517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:34.939336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:39.078025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:29.906152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:33.201609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:36.825721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:40.504921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:44.032052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:47.616138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:51.307601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:54.979557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:58.735785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:02.362267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:05.912819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:09.619257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:13.344983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:16.891582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:20.471875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:24.147005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:27.898146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:31.503545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:35.133505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:39.257398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:30.093257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:33.375262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:36.990551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:40.676738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:44.200182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:47.794225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:51.575954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:55.160849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:58.916036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:02.535090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:06.091511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:09.806123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:13.509457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:17.075797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:20.647873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:24.358391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:28.074652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:31.675525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:35.304905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:39.552810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:30.253449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:33.557442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:37.168159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:40.855481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:44.378503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:47.977138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:51.762080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:55.348947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:59.120560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:02.707897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:06.277457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:10.001245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:13.683251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:17.264073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:20.835492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:24.585938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:28.260263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:31.858652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:35.489414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:39.713776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:30.413909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:33.726795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:37.336082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:41.027683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:44.545594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:48.157537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:51.936492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:55.553781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:59.301069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:02.878049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:06.454799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:10.188058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:13.849310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:17.439940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:21.100916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:24.760882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:28.432821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:32.036539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:35.667011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:39.909647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:30.585287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:33.916568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:37.508325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:41.207356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:44.726402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:48.342124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:52.121441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:55.742376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:59.485273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:03.059128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:06.646532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:10.391675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:14.037221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:17.628055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:21.290803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:24.945776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:28.625590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:32.229662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:35.859547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:40.092225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:30.747044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:34.227382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:37.678342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:41.383529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:44.901177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:48.528426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:52.308474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:55.922153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:59.665843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:03.343550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:06.821567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:10.574241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:14.209043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:17.800894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:21.468918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:25.125660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:28.801530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:32.413083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:36.036895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:40.282649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:30.920990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:34.410593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:37.857558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:41.565119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-20T14:12:43.328370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:46.902717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:50.547813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:54.264975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:58.017939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:01.648319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:05.210706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:08.893217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:12.616431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:16.180548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:19.766156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:23.423710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:27.184827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:30.782571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:34.441443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:38.317933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:42.320221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:32.686224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:36.321397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:39.981224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:43.497781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:47.072828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:50.758898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:54.437930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:12:58.186740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:01.830605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:05.383460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:09.064587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:12.799113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:16.351334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:19.943654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:23.597973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:27.371254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:30.955726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:34.605988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T14:13:38.513726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-20T14:13:51.754579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-20T14:13:52.133357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-20T14:13:52.399526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-20T14:13:52.676789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-20T14:13:52.955689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-20T14:13:42.622897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-20T14:13:43.429122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
00.0000.054.30425.7711242.10200.89850.09590.15170.08150.06790.11020.12620.19350.10770.09910.04470.06290.13720.24420.2702
10.3080.052.19025.7800241.31910.41190.31020.83461.37050.98551.49501.69542.41261.66711.84142.31373.83216.20897.31709.3537
20.6180.052.19025.7800241.16610.21012.86006.660810.87777.357210.599211.595215.424022.704020.401123.442428.195735.003631.991940.9243
30.9260.052.19025.7800241.01410.206913.506026.670739.683823.818137.743338.186344.085050.031544.538152.523553.627555.092945.784158.2230
41.2360.052.19025.7800240.87240.204036.085255.148368.144137.409561.286762.411972.523968.298353.792962.309255.685161.348452.845365.8253
51.5440.052.19025.7800240.98820.202054.833674.344483.050743.359784.108774.225879.076862.014861.355667.664663.403962.219752.494363.3641
61.8540.052.19025.7800241.10480.202069.642376.333281.510044.110579.557180.653182.883064.535657.946065.631859.116063.115852.845364.8363
72.1630.052.19025.7800241.22110.200869.144876.938383.050746.052280.348175.578976.320362.014859.602961.431264.335962.219752.205264.3090
82.4720.052.19025.7800241.21180.200069.642375.034575.163841.418570.107668.119770.193666.662557.582065.098157.556559.235053.141466.8445
92.7800.052.19025.7800241.17250.200160.561567.504769.222841.418569.006065.946365.949762.981658.388859.746264.855858.800750.969562.4461
Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
29538190906.9500.062.0725.1465.54960.200026.098719.754035.867826.528643.027341.954946.973271.935364.183763.212352.974456.519553.501072.4454
29538290907.2580.062.0725.140.10230.200023.004015.278531.834925.771544.812543.293945.266763.521664.725766.083260.760757.672551.862470.0976
29538390907.5670.062.0725.140.06950.200019.565511.659728.883326.440444.352742.593944.085067.165461.355662.309267.301456.921250.053368.3837
29538490907.8760.062.0725.140.03680.199816.50848.830424.486125.588743.460441.720144.085066.662563.212964.659961.617358.443550.969570.6179
29538590908.1860.062.0725.140.00390.200013.71606.713018.559825.771544.812539.506243.198066.662559.602963.212365.295455.474948.914667.3181
29538690908.4950.062.0725.140.00000.200011.41685.194217.745824.804141.376039.125043.637168.932360.948463.715961.225055.474950.969571.7899
29538790908.8020.062.0725.140.00000.20009.44214.076814.988124.634341.376038.920142.345365.582158.388863.212360.760756.921249.477568.9791
29538890909.1120.062.0725.140.00000.19997.67243.279012.638925.150543.460437.669541.524961.585057.946063.715965.295454.778548.662867.3181
29538990909.4210.062.0725.140.00000.20006.33182.701110.426123.977240.046537.322041.524968.932338.152962.798760.379157.260349.220169.4831
29539090909.7290.062.0725.140.00000.20005.20652.28318.693124.301539.844134.888841.126261.585055.221458.505265.295455.092948.914668.3837